A prognostic nomogram to predict overall survival in women with recurrent ovarian cancer treated with bevacizumab and chemotherapy
Objective To develop a nomogram to predict overall survival (OS) in women with recurrent ovarian cancer treated with bevacizumab and chemotherapy. Methods A multicenter retrospective study was conducted. Potential prognostic variables included age; stage; grade; histology; performance status; residual disease; presence of ascites and/or pleural effusions; number of chemotherapy regimens, treatment-free interval (TFI) prior to bevacizumab administration, and platinum sensitivity. Multivariate analysis was performed using Cox proportional hazards regression. The predictive model was developed into a nomogram to predict five-year OS. Results 312 women with recurrent ovarian cancer treated with bevacizumab and chemotherapy were identified; median age was 59 (range: 19-85); 86% women had advanced stage (III-IV) disease. The majority had serous histology (74%), high grade cancers (93.5%), and optimal cytoreductions (69.5%). Fifty-one percent of women received greater than two prior chemotherapeutic regimens. TFI (AHR = 0.98, 95% CI 0.97-1.00, p = 0.022) was the only statistically significant predictor in a multivariate progression-free survival (PFS) analysis. In a multivariate OS analysis, prior number of chemotherapy regimens, TFI, platinum sensitivity, and presence of ascites were significant. A nomogram to predict five-year OS was constructed and internally validated (bootstrap-corrected concordance index = 0.737). Conclusion Our multivariate model identified prior number of chemotherapy regimens, TFI, platinum sensitivity, and the presence of ascites as prognostic variables for OS in women with recurrent ovarian cancer treated with bevacizumab combined with chemotherapy. Our nomogram to predict five-year OS may be used to identify women who may benefit from bevacizumab and chemotherapy, but further validation is needed. © 2014 Elsevier Inc.
Previs, RA; Bevis, KS; Huh, W; Tillmanns, T; Perry, L; Moore, K; Chapman, J; McClung, C; Kiet, T; Java, J; Chan, J; Secord, AA
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